An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat
Multiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective—the value of fighter combat on the basis of the original two-objec...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/8302324 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563047955169280 |
|---|---|
| author | You Li Yingxin Kou Zhanwu Li |
| author_facet | You Li Yingxin Kou Zhanwu Li |
| author_sort | You Li |
| collection | DOAJ |
| description | Multiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective—the value of fighter combat on the basis of the original two-objective model. The new three-objective model includes maximizing the expected damage of the enemy, minimizing the cost of missiles, and maximizing the value of fighter combat. To solve the problem with complex constraints, an improved nondominated sorting algorithm III is proposed in this paper. In the proposed algorithm, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution; otherwise, useless reference points are eliminated. Moreover, an online operator selection mechanism is incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving the problem. Finally, the proposed algorithm is applied to a typical instance and compared with other algorithms to verify its feasibility and effectiveness. Simulation results show that the proposed algorithm is successfully applied to the multiobjective weapon-target assignment problem, which effectively improves the performance of the traditional NSGA-III and can produce better solutions than the two multiobjective optimization algorithms NSGA-II and MPACO. |
| format | Article |
| id | doaj-art-b7f81a46928e4b8fb50373871146c964 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-b7f81a46928e4b8fb50373871146c9642025-02-03T01:21:11ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742018-01-01201810.1155/2018/83023248302324An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter CombatYou Li0Yingxin Kou1Zhanwu Li2Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaMultiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective—the value of fighter combat on the basis of the original two-objective model. The new three-objective model includes maximizing the expected damage of the enemy, minimizing the cost of missiles, and maximizing the value of fighter combat. To solve the problem with complex constraints, an improved nondominated sorting algorithm III is proposed in this paper. In the proposed algorithm, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution; otherwise, useless reference points are eliminated. Moreover, an online operator selection mechanism is incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving the problem. Finally, the proposed algorithm is applied to a typical instance and compared with other algorithms to verify its feasibility and effectiveness. Simulation results show that the proposed algorithm is successfully applied to the multiobjective weapon-target assignment problem, which effectively improves the performance of the traditional NSGA-III and can produce better solutions than the two multiobjective optimization algorithms NSGA-II and MPACO.http://dx.doi.org/10.1155/2018/8302324 |
| spellingShingle | You Li Yingxin Kou Zhanwu Li An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat International Journal of Aerospace Engineering |
| title | An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat |
| title_full | An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat |
| title_fullStr | An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat |
| title_full_unstemmed | An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat |
| title_short | An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat |
| title_sort | improved nondominated sorting genetic algorithm iii method for solving multiobjective weapon target assignment part i the value of fighter combat |
| url | http://dx.doi.org/10.1155/2018/8302324 |
| work_keys_str_mv | AT youli animprovednondominatedsortinggeneticalgorithmiiimethodforsolvingmultiobjectiveweapontargetassignmentpartithevalueoffightercombat AT yingxinkou animprovednondominatedsortinggeneticalgorithmiiimethodforsolvingmultiobjectiveweapontargetassignmentpartithevalueoffightercombat AT zhanwuli animprovednondominatedsortinggeneticalgorithmiiimethodforsolvingmultiobjectiveweapontargetassignmentpartithevalueoffightercombat AT youli improvednondominatedsortinggeneticalgorithmiiimethodforsolvingmultiobjectiveweapontargetassignmentpartithevalueoffightercombat AT yingxinkou improvednondominatedsortinggeneticalgorithmiiimethodforsolvingmultiobjectiveweapontargetassignmentpartithevalueoffightercombat AT zhanwuli improvednondominatedsortinggeneticalgorithmiiimethodforsolvingmultiobjectiveweapontargetassignmentpartithevalueoffightercombat |